RAG with Spring AI

Explores implementing Retrieval-Augmented Generation (RAG) using Spring AI framework for Java developers. Covers setting up a Spring Boot application with OpenAI integration and PostgreSQL/PGVector for vector storage, building a document ingestion pipeline for ETL operations, and using the QuestionAnswerAdvisor API to create context-aware LLM responses. Discusses advantages of Spring AI over LangChain4j for enterprise Java environments, including seamless Spring Boot integration and idiomatic Java patterns. Addresses RAG limitations like retrieval quality dependency, context window constraints, and the need for advanced techniques like re-ranking and hybrid search.